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Competency Framework 2026

AI Literacy
for ICT Apprentices
& All the Other Gen Z's 🫶

Alphas can happily join too — but there's still time for you 😉

A research-backed framework of 23 competencies across 6 domains — designed for first-year Swiss and European ICT apprentices aged 14–19.

23
Competencies
6
Domains
3
Levels
UNESCO AI Competency Framework OECD / EC AILit Framework EU AI Act — Article 4 DigComp 2.2 Chiu et al. 2024
6 Domains of
AI Literacy
Each domain contains competencies at Foundation, Core, and Advanced levels — with concrete "I can" statements for self-assessment.
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Domain 1
Engage with AI
Recognise, understand, and critically evaluate AI in everyday life and ICT work.
E1 Recognise AI in Daily Life & ICT Foundation
Identify where AI is present — from recommendation engines and voice assistants to network monitoring tools and automated deployments.
I can identify at least 10 AI-powered tools I interact with daily
I can distinguish AI systems from traditional rule-based software
I can recognise AI components in ICT infrastructure
E2 Understand How AI Works Foundation
Grasp core concepts: machine learning, training data, models, neural networks, and how generative AI produces outputs.
I can explain the difference between supervised, unsupervised, and reinforcement learning
I can describe how an LLM generates text
I can explain why AI outputs can vary and sometimes 'hallucinate'
E3 Critically Evaluate AI Outputs Core
Assess AI-generated content for accuracy, bias, relevance, and completeness. Never blindly trust AI.
I can fact-check AI-generated code, text, and data
I can identify when an AI response is plausible but incorrect
I can compare outputs from different AI tools to assess reliability
E4 Understand AI's Capabilities & Limitations Core
Know what AI can and cannot do. Understand concepts like context windows, training cutoffs, and domain limitations.
I can explain why AI isn't 'intelligent' the way humans are
I can identify tasks where AI excels vs. where human judgment is essential
I can recognise when an AI tool is not the right solution
Domain 2
Create with AI
Use AI as a collaborative tool for ideation, problem-solving, and professional ICT tasks.
C1 Prompt Engineering Core
Write effective, structured prompts using techniques like role-setting, few-shot examples, chain-of-thought, and iterative refinement.
I can write a structured prompt with role, context, format, and constraints
I can use few-shot examples to guide AI output quality
I can iterate on prompts to progressively improve results
C2 AI-Assisted Professional Work Core
Use AI tools effectively for real ICT tasks: documentation, scripting, debugging, research, and communication.
I can use AI to draft and improve technical documentation
I can use AI to help write, debug, and explain code
I can use AI to research technical solutions
C3 Human-AI Collaboration Core
Know when to delegate to AI and when to rely on your own skills. Maintain ownership of the final output.
I can decide which parts of a task to delegate to AI vs. do myself
I can review, verify, and adapt AI-generated work before submitting it
I can document what AI contributed vs. what I contributed
C4 Creative & Innovative Use of AI Advanced
Go beyond basic use — combine AI tools, chain workflows, and find novel applications in ICT contexts.
I can combine multiple AI tools in a workflow
I can identify opportunities to apply AI in my apprenticeship projects
I can propose AI-enhanced solutions to workplace problems
🛡️
Domain 3
Manage AI
Make responsible decisions about when and how to use AI, including data privacy, security, and workplace policies.
M1 Data Privacy & Security Core
Understand what data you can and cannot share with AI tools. Know the implications under GDPR and Swiss FADP.
I can identify what constitutes sensitive data (personal, company, client)
I can explain why I shouldn't paste confidential data into public AI tools
I can choose appropriate AI tools based on data handling policies
M2 Responsible AI Use in the Workplace Core
Follow company policies on AI use. Know when human oversight is required and when AI assistance is acceptable.
I can follow my company's AI acceptable use policy
I can determine when AI use is appropriate vs. when I need human expertise
I can disclose when AI has been used in my work
M3 AI Tool Selection & Evaluation Advanced
Compare AI tools based on quality, privacy, cost, and suitability for professional use.
I can compare AI tools on criteria like accuracy, privacy, and cost
I can evaluate whether a free AI tool is appropriate for professional use
I can recommend AI tools to colleagues with clear reasoning
M4 Digital Wellbeing & AI Dependency Foundation
Maintain healthy habits around AI use. Avoid over-reliance that could erode core skills.
I can recognise when I'm becoming over-dependent on AI
I can balance AI assistance with building my own foundational ICT skills
I can identify the impact of AI tools on my focus and learning
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Domain 4
AI Ethics & Society
Understand the ethical, legal, and societal dimensions of AI in a Swiss and European context.
ET1 Bias, Fairness & Inclusion Core
Understand how AI can reflect and amplify biases. Recognise the importance of diverse, representative training data.
I can give examples of how bias enters AI systems through training data
I can explain why AI fairness matters in contexts like hiring and lending
I can evaluate an AI tool for potential bias in its outputs
ET2 EU AI Act & Swiss Regulation Core
Understand the EU AI Act's risk-based classification and what it means for ICT professionals in Switzerland and Europe.
I can classify AI applications into the EU AI Act's risk categories
I can explain what 'high-risk AI' means and give Swiss/EU examples
I can describe Switzerland's approach to AI governance
ET3 Transparency, Explainability & Trust Advanced
Understand why AI decisions should be explainable and what transparency means for users and affected people.
I can explain the 'black box' problem in AI
I can argue why transparency matters for AI systems that affect people's lives
I can identify when an AI system lacks sufficient explainability
ET4 Environmental & Societal Impact Advanced
Consider the broader impacts of AI: energy consumption, job displacement, deepfakes, misinformation, and democratic processes.
I can explain the environmental cost of training large AI models
I can discuss how AI affects employment in the ICT sector
I can identify AI-generated content and explain the risks
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Domain 5
Design & Build AI
Understand how AI systems are designed, trained, tested, and deployed — foundational for ICT professionals.
D1 Data Literacy for AI Core
Understand the role of data in AI: collection, cleaning, labelling, and the impact of data quality on model performance.
I can explain why 'garbage in, garbage out' applies to AI
I can describe the steps of preparing data for machine learning
I can identify problems in a dataset
D2 AI System Lifecycle Advanced
Understand the stages of AI development: problem definition, data preparation, model training, testing, deployment, and monitoring.
I can outline the lifecycle of an AI system from idea to production
I can explain why AI models need ongoing monitoring after deployment
I can describe the role of testing and validation
D3 Hands-On AI Experimentation Advanced
Train simple models, experiment with AI tools, and build small AI-powered projects.
I can train a simple image classifier using Teachable Machine
I can experiment with model parameters and observe how results change
I can build a small project that integrates an AI API or tool
D4 Human-Centred AI Design Advanced
Consider user needs, accessibility, and ethical implications when designing AI-powered systems.
I can identify user needs that an AI system should address
I can evaluate whether an AI solution is accessible and inclusive
I can propose safeguards for an AI system
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Domain 6
Future-Ready Mindset
Develop the attitudes and habits needed to keep learning as AI rapidly evolves.
F1 Adaptive Learning & Curiosity Foundation
Stay current with AI developments. Build a habit of continuous learning as tools and capabilities change rapidly.
I can identify reliable sources to stay updated on AI
I can experiment with new AI tools and evaluate their usefulness
I can adapt my workflows as AI capabilities evolve
F2 Career Navigation in an AI World Core
Understand how AI is reshaping ICT careers. Identify skills that remain uniquely human.
I can identify which ICT skills will become more vs. less valuable
I can articulate what humans do better than AI in my field
I can create a personal development plan that accounts for AI's impact
F3 Self-Efficacy & Confidence with AI Foundation
Build confidence in using AI tools. Overcome anxiety about AI while maintaining healthy scepticism.
I feel confident using AI tools in my professional work
I can approach new AI technologies with curiosity rather than fear
I can help others in my team understand and use AI effectively
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3 Mastery Levels
Competencies follow Bloom's taxonomy — from understanding concepts to applying them in professional contexts.
01
Foundation
Know and understand. Recognise AI, grasp core concepts, build confidence, and develop healthy usage habits.
5 competencies — E1, E2, M4, F1, F3
02
Core
Apply and analyse. Use AI effectively for ICT work, evaluate outputs critically, navigate ethics and privacy.
11 competencies — E3, E4, C1–C3, M1, M2, ET1, ET2, D1, F2
03
Advanced
Evaluate and create. Innovate with AI, design human-centred systems, assess societal impact, and shape AI policy.
7 competencies — C4, M3, ET3, ET4, D2, D3, D4
Built on Global Frameworks
This competency framework synthesises insights from leading international AI literacy standards, tailored for the Swiss/EU apprenticeship context.
UNESCO · 2024
AI Competency Framework for Students
12 competencies across 4 dimensions — Human-Centred Mindset, Ethics of AI, AI Techniques & Applications, AI System Design — at Understand, Apply, and Create levels.
OECD / European Commission · 2025
AI Literacy Framework (AILit)
22 competencies across 4 domains — Engage, Create, Manage, Design AI. Forms the basis for PISA 2029 Media & AI Literacy assessment.
European Union · 2024
EU AI Act — Article 4
Mandates AI literacy for all deployers and users of AI systems, including students and educators. Defines risk-based classification for AI applications.
European Commission · 2022
DigComp 2.2
European Digital Competence Framework. Includes AI-specific competencies for citizens covering information literacy, communication, and safety.
Chiu et al. · 2024
AI Literacy & Competency Framework
5 components — Technology, Impact, Ethics, Collaboration, Self-Reflection — with focus on confidence and self-efficacy in AI use for K-12 education.
Switzerland
Swiss FADP & AI Strategy
Switzerland's Federal Act on Data Protection and national AI strategy, ensuring alignment with local data privacy requirements alongside EU frameworks.
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AI-Literate Apprentices?
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